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Cellular Telemetry for Real-Time River Flow Velocity Data from Fluvial Acoustic Tomography Loggers

Year: 2025
Type: dissertation
Abstract: Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents a modular, software-driven telemetry package that enables each FAT logger to autonomously transmit data via a cellular network, eliminating the need for on-site data collection. Each logger was equipped with a PiTalk 4G IoT Dongle, enabling a decentralized architecture that minimizes hardware requirements, reduces potential points of failure, and significantly enhances data availability in remote environments. This dongle incorporates a Quectel EG25-G modem, providing direct cellular connectivity via USB. Custom firmware developed for this project automates modem setup, manages cellular connections, and handles periodic data synchronization with cloud storage using Rclone. The system can be installed entirely offline via a USB drive. Once configured, each logger checks for network connectivity every five minutes and uploads new data every minute. In addition to this specific application, the telemetry system is developed such that integration with other environmental sensors is possible, making it a scalable solution for applications including flood monitoring, hydropower management, and other forms of hydrological monitoring.
Author: Seymour, Joshua Lee
Language: English
Subfield: Geology
Open Access status: green
OpenAlex ID: W6989810101
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